Improved volatiles analysis workflows using automated selected ion flow tube mass spectrometry (SIFT-MS)

Abstract

Selected ion flow tube mass spectrometry (SIFT-MS) is a recent addition to the routine analysis and research laboratory toolkit, primarily as a quantitative tool. SIFT-MS employs ultra-soft chemical ionisation to directly analyse volatile organic compounds (VOCs) in air and headspace in real-time with high specificity and sensitivity. Coupling SIFT-MS with conventional laboratory automation equipment (i.e., that used with chromatography systems) has proved straightforward and enables unattended operation, processing up to 230 samples per day per SIFT-MS instrument. Automated SIFT-MS systems have been applied to analysis of headspace (static, continuous, multiple headspace extraction, and standard additions), sample bags, and thermal desorption tubes. Applications using these approaches include consumer and drug product testing for volatile impurities (such as benzene, formaldehyde, and nitrosamines), environmental samples, clinical research, and materials testing. The stability of the SIFT-MS technique, coupled with its ability to analyse diverse VOCs in a single run, removes the need for system configuration changes and hence reduces calibration demand and streamlines workflows, reducing the time to report the first results in a sequence schedule and increasing sample throughput compared to chromatographic systems. This article reviews the development of the automated-SIFT-MS approach using a variety of application examples and recommends hardware and software improvements that could further enhance its adoption.

Graphical abstract: Improved volatiles analysis workflows using automated selected ion flow tube mass spectrometry (SIFT-MS)

Article information

Article type
Tutorial Review
Submitted
16 Ira. 2024
Accepted
29 Urr. 2024
First published
12 Aza. 2024

Anal. Methods, 2024, Advance Article

Improved volatiles analysis workflows using automated selected ion flow tube mass spectrometry (SIFT-MS)

V. S. Langford and M. J. Perkins, Anal. Methods, 2024, Advance Article , DOI: 10.1039/D4AY01707B

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